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An Introduction to Neural Networks. Prof. Leslie Smith Centre for Cognitive and Computational Neuroscience Department of Computing and Mathematics University of Stirling. lss@cs.stir.ac.uk last major update: 25 October 1996: minor update 22 April 1998 and 12 Sept 2001: links updated (they were out of date) 12 Sept 2001; fix to math font (thanks Sietse Brouwer) 2 April 2003 This document is a roughly HTML-ised version of a talk given at the NSYN meeting in Edinburgh, Scotland, on 28 February 1996, then updated a few times in response to comments received. Please email me comments, but remember that this was originally just the slides from an introductory talk! Why would anyone want a `new' sort of computer?

What is a neural network? Some algorithms and architectures. Where have they been applied? What new applications are likely? Some useful sources of information. Some comments added Sept 2001 NEW: questions and answers arising from this tutorial Why would anyone want a `new' sort of computer? Good at Not so good at Fast arithmetic. Basic Concepts for Neural Networks. Contents Note: This document is an excerpt from the NeuralystTM User's Guide, Chapter 3.

Real Neurons Let's start by taking a look at a biological neuron. Figure 1 shows such a neuron. Figure 1. A neuron operates by receiving signals from other neurons through connections, called synapses. This sounds very simplistic until we recognize that there are approximately one hundred billion (100,000,000,000) neurons each connected to as many as one thousand (1,000) others in the human brain. Each neuron has a body, called the soma. Surrounding the soma are dendrites. If a neuron fires, an electrical impulse is generated. The end of the axon is actually split into multiple ends, called the boutons. At rest, the neuron maintains an electrical potential of about 40-60 millivolts.

It is clear that if signal speed or rate were the sole criteria for processing performance, electronic computers would win hands down.
Artificial neural network. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.

Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons.

This process is repeated until finally, an output neuron is activated. This determines which character was read. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] History[edit] Farley and Wesley A. Models[edit] or both. Neural network.